Summary Brain-computer interface (BCI) technology is currently the subject of intense research interest, as it holds the promise to assist numerous patients suffering from neuromuscular disorders and other systemic and brain diseases. BCI systems allow users to interact with their environment without muscular control, by sensing and decoding the user's thoughts. Noninvasive BCI uses electroencephalography (EEG) to sense user's cognitive states; in EEG, scalp electrodes sense small potential changes arising from underlying neural activity. The effectiveness of BCI, however, depends critically upon training the user's cognitive abilities: users must learn to produce reliable patterns of brain activity that the BCI can decode from its sensors. Some users are able to complete this training with relative ease, but for others the learning process is difficult and lengthy. Thus, methods that can improve training promise to greatly enhance the usefulness of BCI in most applications, benefiting the many patients who may be aided with BCI technology. Mind-body awareness training (MBAT) has shown large effects upon both cognitive abilities and brain activity. MBAT emphasizes meditation practice that focuses on body states, and it has been shown to enhance factors that are most critical for sensorimotor- rhythm-based BCI: sustained attention, motor imagery, and generation of rhythmic neural signals. The general goal of the proposed research is to investigate whether and how experience with MBAT can improve subjects' ability to learn and use a sensorimotor-rhythm-based BCI. The specific aims of the proposed research are as follows: Aim 1: We will test whether MBAT training has significant impact on learning of BCI skills. We will study human subjects with various levels of MBAT experience and compare them with controls. Aim 2: We will use and further develop novel multimodal neuroimaging methods, along with extensive behavioral testing, to identify the neurocognitive components of MBAT that aid learning of BCI skills. Comprehensive analyses will combine imaging and neurocognitive results to identify brain regions responsible for the factors that produce improvement in BCI. The successful completion of the proposed research may allow MBAT training to become a best practice in BCI use, increasing BCI signal quality and reducing its training time. It will also better the understanding on how mind-body intervention works through innovative neuroimaging approaches. Understanding the neurocognitive basis of improvement may allow the production of enhanced training regimens, both with and without MBAT, including MBAT-like training specifically tailored to optimize BCI.